Data QA Engineer

Kellton; Pune District; On-site; INR 1,500,000 - 3,000,000; Full time

Job summary

Kellton is seeking an experienced Data Quality Analyst / Data QA Engineer for its Pune (Kalyani Nagar) team. This WFO role targets professionals with 6-9 years of data quality, testing, and analytics experience to ensure accuracy across source and target systems.

The role emphasizes SQL proficiency, ETL/testing of data pipelines, data profiling, and root-cause analysis. Collaboration with cross-functional teams is essential for maintaining data integrity.

Qualifications

• 6-9 years of experience in Data Quality Analysis, Data QA, Data Testing, or Data Analytics.
• Strong proficiency in advanced SQL and complex query writing.
• Hands-on experience with data validation and reconciliation across source and target systems.
• Experience in ETL testing, data pipeline testing, and data transformation validation.
• Knowledge of Python or PySpark for validation automation is a plus.

Responsibilities

• Perform data validation and reconciliation across source and target systems.
• Write and execute SQL queries, including joins, subqueries, aggregations.
• Conduct ETL testing and validate data pipelines and transformations.
• Perform data profiling and quality assessments.
• Analyze data discrepancies and perform root cause analysis.
• Work with large datasets to identify anomalies and data integrity issues.
• Validate data transformation logic against business rules.
• Prepare and maintain test scenarios, test cases, and defect reports.
• Support data migration validation across platforms and environments.
• Use reporting and data analysis tools to monitor data quality findings.

Skills

SQL

Data validation

Data analysis

Data profiling

Root cause analysis

Data testing

ETL testing

Data reconciliation

Python

PySpark

Tools

Databricks

Cloud platforms (AWS/Azure/GCP)

Job description

Data Quality Analyst / Data QA Engineer

Experience: 6-9 Years

Location: Pune Kalyani Nagar

Work Mode: Work From Office (WFO)

Role Overview

We are looking for experienced Data Quality Analysts / Data QA Engineers with strong analytical skills and expertise in data validation, reconciliation, and data pipeline testing. The ideal candidates will be responsible for ensuring data accuracy, consistency, completeness, and quality across source and target systems.

Key Responsibilities

• Perform data validation and reconciliation across source and target systems to ensure data accuracy and consistency.
• Write and execute advanced SQL queries, including complex joins, subqueries, aggregations, and data comparison queries.
• Conduct ETL testing and validate data pipelines, transformations, and data integration processes.
• Perform data profiling, data quality assessments, and completeness and consistency checks.
• Analyze data discrepancies, investigate issues, and perform root cause analysis.
• Work with large datasets to identify anomalies, inconsistencies, and data integrity issues.
• Validate data transformation logic and ensure adherence to defined business rules.
• Prepare and maintain test scenarios, test cases, validation scripts, and defect reports.
• Support data migration validation and testing activities across dierent platforms and environments.
• Use reporting and data analysis tools to monitor and communicate data quality findings.

Required Skills & Experience

• 6-9 years of experience in Data Quality Analysis, Data QA, Data Testing, or Data Analytics.
• Strong proficiency in advanced SQL and complex query writing.
• Hands-on experience with data validation and reconciliation across source and target systems.
• Experience in ETL testing, data pipeline testing, and data transformation validation.
• Strong understanding of data profiling, data quality analysis, and root cause investigation.
• Ability to work with large datasets and identify data anomalies and inconsistencies.
• Experience with reporting and data analysis tools.
• Strong analytical, problem-solving, and troubleshooting skills.
• Good communication skills and the ability to collaborate with cross-functional teams.

Good to Have

• Knowledge of Python or PySpark for data analysis and validation automation.
• Exposure to Databricks or similar data processing platforms.
• Understanding of cloud-based data platforms such as AWS, Azure, or GCP.
• Experience with data migration testing and validation.
• Familiarity with data warehousing concepts and modern data engineering ecosystems.